The key idea to tackle this problem is to have a controller network which proposes a child model architecture with probability p, given a particular network given in input. The child is trained and evaluated for the particular task to be solved (say for instance that the child gets accuracy R). This evaluation R is passed back to the controller which, in turn, uses R to improve the next candidate architecture. Given this framework, it is possible to model the feedback from the candidate child to the controller as the task of computing the gradient of p and then scale this gradient by R. The controller can be implemented as a Recurrent Neural Network (see the following figure). In doing so, the controller will tend to privilege iteration after iterations candidate areas of architecture that achieve better R and...
TensorFlow 1.x Deep Learning Cookbook
TensorFlow 1.x Deep Learning Cookbook
Overview of this book
Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the artificial intelligence domain.
In this book, you will learn how to efficiently use TensorFlow, Google’s open source framework for deep learning. You will implement different deep learning networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform on
some popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs.
By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning,
GANs, and autoencoders.
Table of Contents (15 chapters)
Preface
Free Chapter
TensorFlow - An Introduction
Regression
Neural Networks - Perceptron
Convolutional Neural Networks
Advanced Convolutional Neural Networks
Recurrent Neural Networks
Unsupervised Learning
Autoencoders
Reinforcement Learning
Mobile Computation
Generative Models and CapsNet
Distributed TensorFlow and Cloud Deep Learning
Learning to Learn with AutoML (Meta-Learning)
TensorFlow Processing Units
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